用张量分解压缩点云，加速近邻搜索。
Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search
- 用张量列车分解低秩表示点云，提升压缩效率。
- 在分布外检测和近邻搜索中表现优于现有方法。
- 揭示了张量车结构的层次性，支持快速检索。
在大规模向量数据库中进行最近邻搜索对多种机器学习应用至关重要。本文提出一种新方法，利用张量列车（Tensor-Train, TT）低秩张量分解高效表示点云，并实现快速近似最近邻搜索。我们引入概率解释，并使用切片沃尔什距离等密度估计损失训练TT分解，实现鲁棒的点云压缩。研究揭示了TT点云中固有的层次结构，从而支持高效的近似最近邻搜索。本文详细阐述了方法原理，并与现有方法进行了全面比较。实验表明该方法在多种场景下有效，包括分布外（OOD）检测任务和近似最近邻（ANN）搜索任务。
原文摘要 · Abstract (English)
Nearest-neighbor search in large vector databases is crucial for various machine learning applications. This paper introduces a novel method using tensor-train (TT) low-rank tensor decomposition to efficiently represent point clouds and enable fast approximate nearest-neighbor searches. We propose a probabilistic interpretation and utilize density estimation losses like Sliced Wasserstein to train TT decompositions, resulting in robust point cloud compression. We reveal an inherent hierarchical structure within TT point clouds, facilitating efficient approximate nearest-neighbor searches. In our paper, we provide detailed insights into the methodology and conduct comprehensive comparisons with existing methods. We demonstrate its effectiveness in various scenarios, including out-of-distribution (OOD) detection problems and approximate nearest-neighbor (ANN) search tasks.
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